Single-cell network biology for resolving cellular heterogeneity in human diseases

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Microfluidic Synthesis, Control, and Sensing of Magnetic Nanoparticles: A Review

Magnetic nanoparticles have attracted significant attention in various disciplines, including engineering and medicine. Microfluidic chips and lab-on-a-chip devices, with precise control over small volumes of fluids and tiny particles, are appropriate tools for the synthesis, manipulation, and evaluation of nanoparticles. Moreover, the controllability and automation offered by the microfluidic chips in combination with the unique capabilities of the magnetic nanoparticles and their ability to be remotely controlled and detected, have recently provided tremendous advances in biotechnology. In particular, microfluidic chips with magnetic nanoparticles serve as sensitive, high throughput, and portable devices for contactless detecting and manipulating DNAs, RNAs, living cells, and viruses. In this work, we review recent fundamental advances in the field with a focus on biomedical applications. First, we study novel microfluidic-based methods in synthesizing magnetic nanoparticles as well as microparticles encapsulating them. We review both continues-flow and droplet-based microreactors, including the ones based on the cross-flow, co-flow, and flow-focusing methods. Then, we investigate the microfluidic-based methods for manipulating tiny magnetic particles. These manipulation techniques include the ones based on external magnets, embedded micro-coils, and magnetic thin films. Finally, we review techniques invented for the detection and magnetic measurement of magnetic nanoparticles and magnetically labeled bioparticles. We include the advances in anisotropic magnetoresistive, giant magnetoresistive, tunneling magnetoresistive, and magnetorelaxometry sensors. Overall, this review covers a wide range of the field uniquely and provides essential information for designing "lab-on-a-chip" systems for synthesizing magnetic nanoparticles, labeling bioparticles with them, and sorting and detecting them on a single chip.

scNetViz: from single cells to networks using Cytoscape

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz-  a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

Synchronous control of magnetic particles and magnetized cells in a tri-axial magnetic field

Precise manipulation of single particles is one of the main goals in the lab-on-a-chip field. Here, we present a microfluidic platform with "T" and "I" shaped magnetic tracks on the substrate to transport magnetic particles and magnetized cells in a tri-axial time-varying magnetic field. The driving magnetic field is composed of a vertical field bias and an in-plane rotating field component, with the advantage of lowering the attraction tendency and cluster formation between the particles compared to the traditional magnetophoretic circuits. We demonstrate three fundamental achievements. First, all the particle movements are synced with the external rotating field to achieve precise control over individual particles. Second, single-particle and single living cell transport in a controlled fashion is achieved for a large number of them in parallel, without the need for a complicated control system to send signals to individual particles. We carefully study the proposed design and introduce proper operating parameters. Finally, in addition to moving the particles along straight tracks, transporting them using a ∼60° bend is demonstrated. The proposed chip has direct applications in the fields of lab-on-a-chip, single-cell biology, and drug screening, where precise control over single particles is needed.

Biomimetic Molecular Clamp Nanopores for Simultaneous Quantifications of NAD+ and NADH

NADH/NAD⁺ is pivotal to fundamental biochemistry research and molecular diagnosis, but recognition and detection for them are a big challenge at the single-molecule level. Inspired by the biological system, here, we designed and synthesized a biomimetic NAD⁺/NADH molecular clamp (MC), octakis-(6-amino-6-deoxy)-γ-cyclomaltooctaose, and harbored in the engineered α-HL(M113R)₇ nanopore, forming a novel single-molecule biosensor. The single-molecule measurement possesses high selectivity and a high signal-to-noise ratio, allowing to simultaneously recognize and detect for sensing NADH/NAD⁺ and their transformations.

Cell-type and fetal-sex-specific targets of prenatal alcohol exposure in developing mouse cerebral cortex

Prenatal alcohol exposure (PAE) results in cerebral cortical dysgenesis. Single-cell RNA sequencing was performed on murine fetal cerebral cortical cells from six timed pregnancies, to decipher persistent cell- and sex-specific effects of an episode of PAE during early neurogenesis. We found, in an analysis of 38 distinct neural subpopulations across 8 lineage subtypes, that PAE altered neural maturation and cell cycle and disrupted gene co-expression networks. Whereas most differentially regulated genes were inhibited, particularly in females, PAE also induced sex-independent neural expression of fetal hemoglobin, a presumptive epigenetic stress adaptation. PAE inhibited Bcl11a, Htt, Ctnnb1, and other upstream regulators of differentially expressed genes and inhibited several autism-linked genes, suggesting that neurodevelopmental disorders share underlying mechanisms. PAE females exhibited neural loss of X-inactivation, with correlated activation of autosomal genes and evidence for spliceosome dysfunction. Thus, episodic PAE persistently alters the developing neural transcriptome, contributing to sex- and cell-type-specific teratology.

•

The neurogenic murine fetal cortex contains about 33 distinct cell subtypes

•

Prenatal Alcohol Exposure (PAE) resulted in sex-specific alterations in developmental trajectory and cell cycle

•

PAE females exhibited neural loss of X-inactivation and spliceosomal dysfunction

•

PAE induced sex-independent neural expression of fetal hemoglobin gene transcripts

The Role of Network Science in Glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

Systems biology analysis identifies TNFRSF9 as a functional marker of tumor-infiltrating regulatory T-cell enabling clinical outcome prediction in lung cancer

Regulatory T cells (Tregs) are enriched in the tumor microenvironment and play key roles in immune evasion of cancer cells. Cell surface markers specific for tumor-infiltrating Tregs (TI-Tregs) can be effectively targeted to enhance antitumor immunity and used for stratification of immunotherapy outcomes. Here, we present a systems biology approach to identify functional cell surface markers for TI-Tregs. We selected differentially expressed genes for surface proteins of TI-Tregs and compared these with other CD4⁺ T cells using bulk RNA-sequencing data from murine lung cancer models. Thereafter, we filtered for human orthologues with conserved expression in TI-Tregs using single-cell transcriptome data from patients with non-small cell lung cancer (NSCLC). To evaluate the functional importance of expression-based markers of TI-Tregs, we utilized network-based measure of context-associated centrality in a Treg-specific coregulatory network. We identified TNFRSF9 (also known as 4-1BB or CD137), a previously reported target for enhancing antitumor immunity, among the final candidates for TI-Treg markers with high functional importance score. We found that the low TNFRSF9 expression level in Tregs was associated with enhanced overall survival rate and response to anti-PD-1 immunotherapy in patients with NSCLC, proposing that TNFRSF9 promotes immune suppressive activity of Tregs in tumor. Collectively, these results demonstrated that integrative transcriptome and network analysis can facilitate the discovery of functional markers of tumor-specific immune cells to develop novel therapeutic targets and biomarkers for boosting cancer immunotherapy.